Language transference rule producing apparatus, language transferring apparatus method, and program recording medium

ABSTRACT

When a portion of an input speech sentence contains an untrained portion or when speech recognition is partly erroneously performed, transference to the target language is disabled. Moreover, a phrase dictionary and interphrase rules which are necessary for transference must be manually produced. Therefore, development is low in efficiency and requires much labor. An apparatus includes: a language rule producing section which statistically automatically trains grammatical or semantic restriction rules for a partial word or a word string from a parallel-translation corpus, and in which rules are described in the form wherein a source language partial sentence corresponds to a target language partial sentence; a speech recognizing section which performs speech recognition on speech of the source language by using the produced language rules, and which outputs a result of the recognition; and a language transferring section which transfers a source language sentence into a target language sentence by using the same language rules. Even when a portion of an input speech sentence contains an untrained portion or when speech recognition is partly erroneously performed, transference to the target language is surely enabled. Moreover, a phrase dictionary and interphrase rules which are necessary for transference can be automatically produced without requiring much manual assistance.

This application is a continuation of U.S. patent application Ser. No.09/701,921, filed Dec. 4, 2000, which is a National Phase ofPCT/JP99/02954, filed Jun. 2, 1999. U.S. patent application Ser. No.09/701,921 is hereby incorporated by reference.

TECHNICAL FIELD

The invention relates to a language transferring apparatus whichtransfers input speech or an input text into another language or anotherliterary style, and also to a language transference rule producingapparatus method which produces transference rules for the same.

BACKGROUND ART

Hereinafter, the conventional art will be described with taking as anexample an apparatus which is one of language transferring apparatuses,and which translates input speech into another language (hereinafter,referred to as interpretation).

In an interpreting apparatus, interpretation is realized by sequentiallyinterpreting speech recognition for transferring an uttered sentencewhich is input as a sound signal into an output sentence that isindicated by a word text string, and language translation which receivesthe sentence indicated by the word text string, and then translates itinto a sentence of another language. The language translating section isconfigured by: a language analyzing section which analyzes the syntacticor semantic structure of the input sentence; a language transferringsection which transfers the input sentence into another language on thebasis of a result of the analysis; and an output sentence producingsection which produces a natural output sentence from a result of thetranslation.

In a case where the speech recognizing section erroneously recognizes apart of the uttered sentence, or a case where the uttered sentenceitself is unnatural in both syntactic and semantic meanings, such asthose where chiming, restating, or the like is inserted into thesentence, or where utterance is ended while the sentence has not yetbeen completed, however, there arises a problem in that, even when aresult of speech recognition is input into the language analyzingsection, analysis is failed and therefore a result of translation is notoutput.

In order to solve the problem, a configuration is proposed in which asentence is divided into phrases, intraphrase rules and interphraserules are separately made, and incomplete utterance is analyzed by usingonly the intraphrase rules, thereby enabling a result of the analysis tobe output. (For example, Takezawa and Morimoto: The Transaction of theInstitute of Electronics and Communication Engineers D-II, Vol.J79-D-II(12)) FIG. 14 shows an example of intraphrase and interphraserules of the conventional art. In this example, with respect to a corpusexample 301 of “KONBAN, SINGLE NO HEYA NO YOYAKAU ONEGAI NE”,intraphrase rules are described in a tree structure such as intraphraserules 302, on the basis of grammar rules which are common also towritten language, and interphrase rules are described in the term ofadjacency probability among phrases in a training corpus. For example,the interphrase rules are described as shown in interphrase rules 303.

When an input sentence is to be analyzed, the intraphrase rules aresequentially applied to phrases with starting from the beginning of thesentence. The input sentence is analyzed while the phrases are connectedto one another so that, for each phrase, phrase candidates of higheradjacency probability are adjacent to each other. In this sentenceanalyzing method, even when a part of a sentence is erroneouslyrecognized and usual analysis of the whole sentence fails, phrases ofthe portion which does not include erroneous recognition can becorrectly analyzed. Therefore, a scheme is made so that a translationresult can be Partially output by translating only the analyzed partialphrases.

In order to solve the problem, another method is proposed in which,unlike the conventional art in which language analysis is performed inaccordance with the grammar, parallel-translation phrases ofcorresponding source language and target language sentences areextracted from uttered sentence examples including uttered sentenceswhich cannot be analyzed by the conventional grammar, aparallel-translation phrase dictionary in which the phrase pair isdescribed in a form that is generalized as far as possible is produced,and language analysis and language transference are performed by usingthe dictionary. (For example, Furuse, Sumida, and Iida: The Transactionof Information Processing Society of Japan Vol 35, no 3, 1994-3) FIG. 15shows a language transference rule producing apparatus of theconventional art. Before interpretation is performed, aparallel-translation phrase dictionary is previously produced from anuttered sentence parallel-translation corpus. Also in this method, inconsideration a case where a part of words are erroneous or omitted, anuttered sentence is divided into phrases, and intraphrase rules anddependency rules between the phrases are produced. First, amorphological analyzing section 360 analyzes morphemes of the sourcelanguage sentence and the target language sentence, and transfers thesentences into morpheme strings. Next, a phrase determining section 361divides the morpheme strings of the source language and the targetlanguage in the unit of phrase, and then produces intraphrase rules anddependency relationship rules between the phrases. In this case, eachphrase unit is manually determined in consideration that, in partialsentences, the correspondence relationships in the parallel translationare apparent, in addition that each phrase unit is a unit which issemantically consistent. For example, a parallel-translation sentenceexample of “HEYA NO YOYAKU O ONEGAISHITAINDESUGA” and “I'd like toreserve a room” are divided into two parallel-translation phrases (a)and (b), or (a) “HEYA NO YOYAKU” and “reserve a room”, and (b) “OONEGAISHITAINDESUGA” and “I'd like to”, and a dependency relationship of“(a) O (b) SURU” and “(b) to (a)” is regularized. Theparallel-translation phrases are stored in a parallel-translation phrasedictionary 362, and the dependency relationship between the phraseswhich is expressed in the form of parallel translation is stored in aninterphrase rule table 363. This process is performed on all utteredsentences included in the parallel-translation corpus. This division anddependency relationship of phrases are determined depending on semanticinformation of a sentence and factors such as the degree at which thesentence is ungrammatical. Therefore, it is difficult to automaticallydetermine them for each sentence. Conventionally, consequently, they aremanually determined.

In the sentence analyzing means of the first conventional example,however, phrases to be handled are language-dependent phrases which aredependent only on the source language, and often fail to coincide withphrase units of the target language. Therefore, the means has a problemin that, even when phrases which are correct in the source language areinput into the language transferring section, it is often that thephrases cannot be finally accepted. The scheme of the first conventionalexample is enabled also by using language-independent phrases. In thiscase, analysis of language-independent phrases must be manuallyproduced, thereby causing further problems in that the developmentrequires a lot of time, and that rule performances are distorted byswinging of criteria of the manual production.

In the method of producing a parallel-translation phrase dictionary inthe second conventional example, there is no means for automaticallyanalyzing semantic information and grammatical information of an utteredsentence, and hence such information must be manually produced.Therefore, the method has problems in that the development requires alot of time, and that rule performances are distorted by swinging ofcriteria of the manual production. When the target task of aninterpreting apparatus is changed, or when the kinds of the sourcelanguage and the target language are changed, rules which have been onceestablished cannot be applied, and all of the rules must be againproduced. Therefore, the development is low in efficiency andcumbersome.

In the phrase dictionary 362 and the interphrase rule table 363, aphrase unit is determined with placing emphasis on the correspondencerelationships of the parallel-translation corpus, and the phrase unit isnot evaluated whether it is adequate for recognition by the speechrecognizing section 364 or not. It is difficult to determine a phraseunit while manually judging whether the phrase is adequate for speechrecognition or not. The method has a problem in that, when recognitionis performed by using the determined phrase, it is not guaranteed toensure the recognition rate.

DISCLOSURE OF INVENTION

It is an object of the invention to provide a language transferringapparatus and method which can solve the above-discussed problems, inwhich, even when an input speech sentence contains an untrained portionor when speech recognition is partly erroneously performed, transferenceto the target language is surely enabled, and in which a phrasedictionary and interphrase rules required for transference can beautomatically produced without requiring much manual assistance.

In order to solve the problems, a first aspect of the invention isdirected to a language transferring apparatus characterized in that theapparatus comprises: storing means for storing language rules which areobtained by training grammatical or semantic restriction rules for aword or a word string from a training database in which a sentence thatis input in a form of speech or text, and that is a target languagetransference (hereinafter, such a sentence is referred to as a sourcelanguage sentence, and a sentence that has undergone languagetransference correspondingly with it is referred to as a target languagesentence) is paired with a target language sentence (hereinafter, such adatabase is referred to as a parallel-translation corpus);

a speech recognizing section which performs speech recognition on inputspeech by using the stored language rules, and which outputs a result ofthe recognition in a form of a sentence that is a target languagetransference; and

a language transferring section which transfers a sentence that is atarget language transference, into a sentence that has undergonelanguage transference, by using the same language rules as that used inthe speech recognizing section.

Furthermore, a second aspect of the invention is directed to a languagetransferring apparatus according to the first aspect of the inventionand characterized in that the language rules are produced by dividingthe sentence that is a target language transference, and the transferredsentence into portions in which both the sentences form semanticconsistency (referred to as style-independent phrases), and making ruleswith separating language rules in the style-independent phrases fromlanguage rules between the style-independent phrases.

Furthermore, a third aspect of the invention is directed to a languagetransferring apparatus according to the second aspect of the inventionand characterized in that the language rules are produced by makingrules on grammatical or semantic rules in the style-independent phrasesand concurrent or connection relationships between the style-independentphrases.

Furthermore, a fourth aspect of the invention is directed to a languagetransferring apparatus according to the first aspect of the inventionand characterized in that the apparatus comprises a speech synthesizingsection which performs speech synthesis on the sentence that hasundergone language transference, by using a same language rules as thatused in the language transferring section.

Furthermore, a fifth aspect of the invention is directed to a languagetransferring apparatus according to any of the first to fourth aspectsof the invention and characterized in that the apparatus comprises: arule distance calculating section which, for a language rule group whichis obtained by, among the language rules, bundling language rules of asame target language sentence as a same category, calculates an acousticrule distance of the sentence that is a target language transference oflanguage rules contained in the language rule group; and

an optimum rule producing section which, in order to enhance arecognition level of speech recognition, optimizes the rule group bymerging language rules having a similar calculated distance.

A sixth aspect of the invention is directed to a language transferencerule producing apparatus and characterized in that the apparatuscomprises: a parallel-translation corpus;

a phrase extracting section which calculates a frequency of adjacency ofwords or parts of speech in a source language sentence and a targetlanguage sentence in the parallel-translation corpus, and couples wordsand parts of speech of a high frequency of adjacency to extract partialsentences in each of which semantic consistency is formed (hereinafter,such a partial sentence is referred to as a phrase);

a phrase determining section which, among the phrases extracted by thephrase extracting section, checks relationships between phrases of thesource language and the target language with respect to a whole of asentence to determine corresponding phrases; and

a phrase dictionary which stores the determined corresponding phrases,

the phrase dictionary is used when language transference is performed,and the language transference, when a source language sentence is input,matches the input sentence with the corresponding phrases stored in thephrase dictionary, thereby performing language or style transference.

Furthermore, a seventh aspect of the invention is directed to a languagetransference rule producing apparatus according to the sixth aspect ofthe invention and characterized in that the phrase determining sectionchecks concurrent relationships between phrases of the source languageand the target language, thereby determines corresponding phrases.

Furthermore, an eighth aspect of the invention is directed to a languagetransference rule producing apparatus according to the sixth aspect ofthe invention and characterized in that the apparatus further has: amorphological analyzing section which transfers the source languagesentence of the parallel-translation corpus into a word string; and

a word clastering section using part-of-speech which, by using a resultof the morphological analyzing section, produces a parallel-translationcorpus in which words of a part or all of the source language sentenceand the target language sentence are replaced with speech part names,and

the phrase extracting section extracts phrases from theparallel-translation corpus in which words are replaced with speech partnames by the word clastering section using part-of-speech.

Furthermore, a ninth aspect of the invention is directed to a languagetransference rule producing apparatus according to the eighth aspect ofthe invention and characterized in that the apparatus has aparallel-translation word dictionary of the source language and thetarget language, and

the word clastering section using part-of-speech replaces words whichare corresponded in the parallel-translation word dictionary and inwhich the source language is a content word, with speech part names.

Furthermore, a tenth aspect of the invention is directed to a languagetransference rule producing apparatus according to the sixth aspect ofthe invention and characterized in that the apparatus further has: amorphological analyzing section which transfers the source languagesentence of the parallel-translation corpus into a word string; and

a semantic coding section which, by using a result of the morphologicalanalyzing section, on a basis of a table in which words are classifiedwhile deeming words that are semantically similar, to be in a sameclass, and a same code is given to words in a same class (hereinafter,such a table is referred to as a classified vocabulary table), producesa parallel-translation corpus in which words of a part or all of thesource language sentence and the target language sentence are replacedwith codes of the classified vocabulary table, and

the phrase extracting section extracts phrases from theparallel-translation corpus in which words are replaced with codes bythe semantic coding section.

Furthermore, an eleventh aspect of the invention is directed to alanguage transference rule producing apparatus according to the tenthaspect of the invention and characterized in that the apparatus has aparallel-translation word dictionary of the source language and thetarget language, and

the semantic coding section replaces only words which are correspondedin the parallel-translation word dictionary, with semantic codes.

Furthermore, a twelfth aspect of the invention is directed to a languagetransference rule producing apparatus according to the sixth aspect ofthe invention and characterized in that the phrase extracting sectionextracts phrases by using also a phrase definition table whichpreviously stores word or sentence part strings that are wished to bepreferentially deemed as a phrase, with pairing the source language andthe target language.

Furthermore, a thirteenth aspect of the invention is directed to alanguage transference rule producing apparatus according to any one ofthe sixth to thirteenth aspects of the invention and characterized inthat the apparatus has a perplexity calculating section which calculatesa perplexity of a corpus, and

the phrase extracting section extracts phrases by using a frequency ofadjacency of words or word classes, and the perplexity.

Furthermore, a fourteenth aspect of the invention is directed to aprogram recording medium characterized in that the medium stores aprogram for causing a computer to execute functions of a whole or a partof components of the language transferring apparatus or the languagetransference rule producing apparatus according to any one of the firstto thirteenth aspects of the invention.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram showing the configuration of a languagetransferring apparatus in a first embodiment of the invention.

FIG. 2 is a block diagram showing the configuration of a languagetransferring apparatus in a second embodiment of the invention.

FIG. 3 is a view illustrating production of language rules in the firstembodiment of the invention.

FIG. 4 is a view illustrating production of optimum language rules inthe second embodiment of the invention.

FIG. 5 is a block diagram showing the configuration of a languagetransferring apparatus and a language rule producing apparatus in athird embodiment of the invention.

FIG. 6 is a view illustrating production of language transference rulesin the third embodiment of the invention.

FIG. 7 is a view showing an example of a parallel-translationinterphrase rule table and a parallel-translation phrase dictionary inthe third embodiment of the invention.

FIG. 8 is a block diagram showing the configuration of a languagetransferring apparatus and a language rule producing apparatus in afourth embodiment of the invention.

FIG. 9 is a view showing an example of a phrase definition table in thefourth embodiment of the invention.

FIG. 10 is a block diagram showing the configuration of a languagetransferring apparatus and a language rule producing apparatus in afifth embodiment of the invention.

FIG. 11 is a view illustrating production of language rules in the fifthembodiment of the invention.

FIG. 12 is a block diagram showing the configuration of a languagetransference rule producing apparatus in a sixth embodiment of theinvention.

FIG. 13 is a block diagram showing an example of the configuration of alanguage transferring apparatus having a speech synthesizing section.

FIG. 14 is a view showing an example of language rules used in aconventional language transferring apparatus.

FIG. 15 is a block diagram showing the configuration of a conventionallanguage transferring apparatus.

DESCRIPTION OF THE REFERENCE NUMERALS AND SIGNS

-   1 parallel-translation corpus-   2 language rule reproducing section-   3 intraphrase language rule-   4 interphrase language rule-   5 sentence production rule-   6 microphone-   7 speech recognizing section-   8 acoustic model-   9 language transferring section-   10 output sentence producing section-   101 parallel-translation corpus-   102 morphological analyzing section-   103 content word definition table-   104 word clastering section using part-of-speech-   105 phrase extracting section-   106 phrase determining section-   107 parallel-translation word dictionary-   108 parallel-translation interphrase rule table-   109 parallel-translation phrase dictionary-   110 speech recognition-   111 language transference-   112 output sentence production-   113 acoustic model-   114 sentence production rule

BEST MODE FOR CARRYING OUT THE INVENTION

Hereinafter, embodiments of the invention will be described withreference to the drawings.

First Embodiment

First, a first embodiment will be described.

In the first embodiment, description will be made by using, as anexample of a language transferring apparatus, an interpreting apparatuswhich performs transference between different languages, in the samemanner as the conventional art examples. FIG. 1 is a block diagram ofthe interpreting apparatus of the embodiment.

In the interpreting apparatus of the embodiment, before interpretationis performed, a language analyzing section 2 previously trains languagerules of the source language sentence and the target language sentenceof an uttered sentence, from a training database 1 which has aparallel-translation corpus, a parallel-translation word dictionary, andthe like. FIG. 3 shows an example of training of the language rules.

In the language rule producing section 2, content words of the sourcelanguage and the target language are replaced with speech part names byusing, for example, a parallel-translation corpus to which speech parttags are given. In the case where a phrase in the source language andthat in the target language correspond to each other as one bundle, theone bundle is set as style-independent phrases and the boundary isdelimited. Namely, in the case where a style-dependent phrase in thesource language and that in the target language correspond to each otheras one bundle, the one bundle is set as the boundary of astyle-independent phrases. In the case where a style-dependent phrase inthe target language corresponding to that in the source language do notcorrespond as one bundle, coupling of style-dependent phrases andcorrection of the phrase boundary are performed until correspondingportions exist as one bundle, thereby setting the phrases asstyle-independent phrases. Referring to FIG. 3, sentences of theparallel-translation corpus, “KONBAN, HEYA NO YOYAKU O SHITAINDESUGA”and “I'd like to room-reservation tonight” 26 are replaced with speechpart names by replacement of content words with speech part names 30, as“<common noun>|<common noun> NO <“S” series irregular conjugationnoun>|O OSHITAINDESUGA” 27. Furthermore, boundaries are delimited asstyle-independent phrases, or as “<common noun>”, “<common noun> NO <“S”series irregular conjugation noun>”, “O SHITAINDESUGA”. In eachstyle-independent phrase, thereafter, a mixed string of speech partnames and words, the name of word of a portion indicted by the speechpart name, and the frequency of occurrence in the parallel-translationcorpus of each style-independent phrase are described asstyle-independent intraphrase rules 3. For all sentences of theparallel-translation corpus, the above-mentioned rules are described. InFIG. 3, the above-mentioned contents are described in 3 by descriptionof intraphrase rules 31. In 3 of FIG. 3, rule 1 has |<common noun>| forJapanese, and |<noun>| for English. The speech part contents are“KONBAN” for Japanese, and “tonight” for English. If appearing in theparallel-translation corpus, also “ASU”, “tomorrow”, and the like aredescribed in rule 1.

Moreover, concurrent relationships of each intraphrase rule aredescribed as style-independent interphrase rules 4. When concurrentrelationships are to be regularized as a phrase bi-gram, for example,the frequency of adjacency of style-independent phrases are previouslydescribed.

The above-described contents mean that, in FIG. 3, description ofinterphrase rules 32 describes 28. 28 is an example of a phrase bi-gram.For example, a rule number pair is “(rule 1) (rule 2)” and its frequencyof occurrence is 4. This means that cases where, during a process oftraining from the parallel-translation corpus, rule 1 and rule 2 appearside by side in the sentence occurred four times. In the example of 28,cases where rule 2 and rule 3 appear side by side in the sentenceoccurred six times.

Moreover, also the syntax structures between style-independent phrasesare described in the style-independent interphrase rules 4. This meanthat, in FIG. 3, description of interphrase rules 32 describes 29. Sincethe appearance sequence of style-independent phrases in Japanese isdifferent from that in English, the description of interphrase rules 32make sequence relationships to correspond with one another by expressingthe language structures in a tree form in 25.

In sentence production rules 5, target language rules which lack in thelanguage rules 3 and 4 are described. In the case of Japanese-Englishtranslation, for example, article and indefinite article rules, thirdperson singular rules, and the like are described as the contents of thesentence production rules.

The intraphrase language rules 3 and/or the interphrase language rules 4constitute an example of the storing means in the invention.

In the case of interpretation, uttered speech of the source language isfirst input through a microphone 6 into a speech recognizing section 7.The speech recognizing section predicts sequentially candidates for arecognized word in time sequence, from the mixed string of speech partnames and words described as the style-independent intraphrase languagerules 3, and the phrase bi-gram serving as the style-independentinterphrase language rules 4. A sum of an acoustic score based on thedistance value between a previously trained acoustic model 8 and theinput speech, and a language score based on the phrase bi-gram is set asa recognition score, and a continuous word string serving as arecognition candidate is determined by Nbest-search. The thus determinedcontinuous word string is input into a language transferring section 9.In the intraphrase language rules 3 and the interphrase language rules4, the rules are previously established while the source language andthe target language correspond to each other. In the languagetransferring section 9, the continuous word string is transferred intophrase strings of the target language by using the rules, and thenoutput. In this case, when the input phrase string of the sourcelanguage coincides with the syntax structure between phrases which havebeen already trained, the phrase string of the target language iscorrected in accordance with the syntax structure and then output.

The output target language sentence is input into an output sentenceproducing section 10, and grammatical unnaturalness is corrected. Forexample, optimizations such as addition of articles and indefinitearticles, and transference of a verb into the third person singularform, the plural form, the past form in a pronoun and a verb, or thelike are performed. The corrected translation resultant sentence of thetarget language is output, for example, in the form of a text.

In the embodiment described above, when the language rules used inspeech recognition are to be trained, the rules are produced whilebundled portions in which both the source language and the targetlanguage have meaning are used as one unit, and recognition is performedon the basis of restrictions of the rules. Therefore, it is possible torealize a language transferring apparatus which can solve the problemthat, when an input speech sentence contains an untrained portion orspeech recognition is partly erroneously performed, any portion of atranslation result of the whole sentence is not output, and which canoutput an adequate translation result with respect to a portion that hasbeen correctly recognized.

In the embodiment, the interpreting apparatus has been described as anexample of the language transferring apparatus. This can be similarlyused in another language transferring apparatus, for example, a languagetransferring apparatus which transfers an unliterary uttered sentenceinto a text sentence in written language.

Second Embodiment

Next, a second embodiment will be described with reference to thedrawings. In the embodiment also, in the same manner as the firstembodiment, description will be made by using an interpreting apparatus.FIG. 2 is a block diagram of the interpreting apparatus of theembodiment.

In the interpreting apparatus of the embodiment, before interpretationis performed, a language rule producing section 11 previously trainsintraphrase language rules 12 and interphrase language rules 13 of thesource language sentence and the target language sentence of an utteredsentence, from a training database 1 which has a parallel-translationcorpus and a parallel-translation word dictionary. The trained rules areidentical with the training of the language rules in the firstembodiment. Next, the trained language rules are optimized. FIG. 4 showsan example of the optimization.

Among the trained style-independent phrases, phrases of the same targetlanguage are bundled as the same category. Referring to FIG. 4, 12denotes language rules. The language rules are bundled by rule distancecalculation 14, as categories as indicated in 33. Rule 1, rule 2, andrule 3 have the same target language rule of “I'd like to”, and hencethe rules are set into the same category. Since rule 4 has a targetlanguage rule of “please”, the rule is classified into a categorydifferent from that of rule 1, rule 2, and rule 3. Next, the ruledistance calculating section 14 calculates the acoustic distance betweensource language phrases contained in the same category. In FIG. 4, 15shows examples of the calculated acoustic distance between sourcelanguage phrases. In 15, the distance between rule 1 and rule 2 is 7,and the distance between rule 1 and rule 3 is 2.

The acoustic distance of the source language phrases contained in thesame category rule is calculated in the following manner. First, whenthe parts of sentence are identical with each other, the same word isapplied to sentence part portions of the mixed string in all the targetlanguage phrases in the category, and all the mixed strings aretransferred into word strings. In order to check whether the wordstrings are similar in pronunciation or not, the distance with respectto a difference in a character string of each word string is thencalculated by using (Ex. 1), and then written into the rule distancetable 15. When the distance between phrase X={x1, x2, x3, . . . xn)(where x indicates each word) consisting of an n number of words, andphrase Y={y1, y2, y3, . . . ym) consisting of an m number of words isindicated by D(Xn, Ym), $\begin{matrix}{{{D( {{Xi},{Yj}} )} = {\min{\begin{matrix}{D( {{{Xi} - 1},{Yj}} )} & {+ {d( {{xi},{yj}} )}} \\{D( {{{Xi} - 1},{{Yj} - 1}} )} & {+ {d( {{Xi},{Yj}} )}} \\{D( {{Xi},{{Yj} - 1}} )} & {+ {d( {{Xi},{Yj}} )}}\end{matrix}}}}{{{where}\quad{if}\quad{xi}} = {{{yj}\quad{then}\quad{d( {{xi},{yj}} )}} = 0}}{{{else}\quad{d( {{xi},{yj}} )}} = 1}} & \lbrack {{Ex}.\quad 1} \rbrack\end{matrix}$

In an optimum rule producing section 16, only the rule of the largestnumber of occurrences in phrases having a distant value which is notlarger than a fixed value is left, and all the other rules are erased.In the example of FIG. 4, for example, when the fixed value is set to 2,the rule distance between rule 1 and rule 3 which are in the samecategory in 33 is 2, or not larger than the fixed value of 2. In the tworules, therefore, rule 1 having a higher frequency of occurrence isadopted, and rule 3 is deleted from the rules. In accordance with theabove, the number of occurrences is rewritten.

After the above-mentioned rule optimization is performed on all therules written in the intraphrase language rules 12, only language ruleswhich have not been erased are stored as intraphrase optimum languagerules 17. In accordance with the optimized rules, the removed rules inthe interphrase rules 13 are rewritten with the employed rules, and alsothe number of occurrences is corrected. Referring to FIG. 4, rule 3 isdeleted by optimum rule production 16, and united with rule 1. Inaccordance with this, as indicated 17, the occurrence number of rule 1is set to 15 which is a sum of the rule and rule 3 that has beendeleted.

In sentence production rules 5, target language rules which lack in thelanguage rules produced from the corpus are described. In the case ofJapanese-English translation, for example, article and indefinitearticle rules, and third person singular rules, etc. are described asthe contents of the sentence production rules.

In the case of interpretation, uttered speech of the source language isfirst input through a microphone 6 into a speech recognizing section 7.The speech recognizing section predicts sequentially candidates for arecognized word in time sequence, from the mixed string of speech partnames and string words described as the style-independent intraphraseoptimum language rules 17, and the frequency of adjacency of phrases asstyle-independent interphrase optimum language rules 18. A sum of anacoustic score based on the distance value between a previously trainedacoustic model 8 and the input speech, and a language score based on aphrase bi-gram is set as a recognition score, and a continuous wordstring serving as a recognition candidate is determined by Nbest-search.The thus determined continuous word string is input into a languagetransferring section 9. In the language rules 17 and 18, the rules arepreviously established while the source language and the target languagecorrespond to each other. In the language transferring section 9, thecontinuous word string is transferred into phrase strings of the targetlanguage by using the rules, and then output. In this case, when theinput phrase string of the source language coincides with the syntaxstructure between phrases which has been already trained, the phrasestring of the target language is corrected in accordance with the syntaxstructure and then output.

The output target language sentence is input into an output sentenceproducing section 10, and grammatical unnaturalness is corrected. Forexample, optimizations such as addition of articles and indefinitearticles, and transference of a verb into the third person singularform, the plural form, or the past form in a pronoun and a verb areperformed. The corrected translation resultant sentence of the targetlanguage is output, for example, in the form of a text.

In the embodiment described above, when the language rules used inspeech recognition are to be trained, the rules are produced whilebundled portions in which both the source language and the targetlanguage have meaning are used as one unit. Thereafter, when sourcelanguage phrases having the same ruled target language portion areacoustically similar to one other, only the rule of the highestfrequency of occurrence is adopted from the similar rules, and theremaining rules are erased. As a result, it is possible to realize aninterpreting apparatus in which the increase of the number of rules dueto the setting of a style-independent phrase as a unit is suppressedwithout lowering the performance of the language rules as far aspossible, and therefore recognition and language transference of highperformance are enabled.

In the embodiment, the interpreting apparatus has been described as anexample of the language transferring apparatus. This can be similarlyused in another language transferring apparatus, for example, a languagetransferring apparatus which transfers an unliterary uttered sentenceinto a text sentence in written language.

Embodiment 3

In the embodiment, description will be made by, as an example of alanguage transferring apparatus, using an interpreting apparatus whichperforms transference between different languages, in the same manner asthe conventional art examples. FIG. 5 is a block diagram of theinterpreting apparatus of the embodiment.

In the embodiment, a parallel-translation corpus 101, a content worddefinition table 103, a parallel-translation word dictionary 107, amorphological analyzing section 102, a word clastering section usingpart-of-speech 104, a phrase extracting section 105, a phrasedetermining section 106, a parallel-translation interphrase rule table108, and a parallel-translation phrase dictionary 109 constitute anexample of the language transference rule producing apparatus of theinvention. The parallel-translation phrase dictionary 109 of theembodiment is an example of the phrase dictionary set forth in claim 6of the invention.

In the interpreting apparatus of the embodiment, before interpretationis performed, the morphological analyzing section 102 analyzes morphemesof the source language sentence in the parallel-translation corpus 101,thereby producing a parallel-translation corpus in which a speech parttag is given only to the source language sentence. For example, in anexample of an uttered speech 120 of “HEYA NO YOYAKU OONEGAISHITAINDESUGA” of FIG. 6, speech part tags as shown in 121 aregiven to the source language sentence. Next, the word clastering sectionusing part-of-speech 104 produces a speech part parallel-translationcorpus in which a part of word names in the source language sentenceprovided with speech part tags in the corpus are replaced with speechpart names. In this case, it is assumed that a word which is to bereplaced with a speech part name satisfies the following conditions.

(1) The word corresponds to a part of sentence listed in a content wordtable.

(2) A word which is registered in the parallel-translation worddictionary, and which corresponds to the target language translation inthe parallel-translation word dictionary exists in a correspondingparallel-translation sentence of the target language in the corpus.

In the example of the content word definition table 103 of FIG. 6, amongcommon nouns, “S” series irregular conjugation nouns, and verbs listedin the content word table, only “HEYA” and “YOYAKU” registered in theparallel-translation word dictionary 107 are replaced with parts ofsentences, so that a corpus in which these words are replaced withspeech part names is produced as shown in 122. Furthermore, also thecorresponding word names in the parallel-translation sentence of thetarget language are replaced with speech part names in Japanese.

With respect to the corpus in which a part of word names are replacedwith speech part names, the phrase extracting section 105 calculates afrequency of doubly chained occurrence (hereinafter, referred to asbi-gram) of each word or part of speech. The source language sentenceand the target language sentence are separately subjected to thiscalculation. The calculation expression is shown in (Ex. 2).$\begin{matrix}\frac{\begin{matrix}\{ {{number}\quad{of}\quad{cases}\quad{in}\quad{which}\quad{word}}\quad  \\{( {{or}\quad{part}\quad{of}\quad{speech}} )i\quad{and}\quad{word}} \\ {( {{or}\quad{part}\quad{of}\quad{speech}} )j\quad{occur}\quad{adjacently}} \}\end{matrix}}{\begin{matrix}\{ {{total}\quad{number}\quad{of}\quad{occurrences}\quad{of}\quad{word}}  \\{{{( {{or}\quad{part}\quad{of}\quad{speech}} )i} + {{total}\quad{number}\quad{of}}}\quad} \\ {{occurences}\quad{of}\quad{word}\quad( {{or}\quad{part}\quad{of}\quad{speech}} )j} \}\end{matrix}} & ( {{Ex}.\quad 2} )\end{matrix}$

After a bi-gram is calculated for all of the source language sentencesand the target language sentences in the corpus, the phrase extractingsection 5 couples two words or a speech part pair of the highestfrequency of occurrence to each other, while assuming the words or thepair as one word. Then, a bi-gram is again calculated. As a result, forexample, word pairs such as “O” and “NEGAI”, “NEGAI” and “SHI”, and“SHI” and “MASU” in each of which the words are adjacent at a higherfrequency are coupled to one another to form a phrase candidate“ONEGAISHIMASU”. In the target language, the word pairs of “I'd” and“like”, and “like” and “to” are coupled to each other. For each of allof the source language sentences and the target language sentences, theabove-mentioned coupling and calculation of a bi-gram are repeated untilthe values of all bi-grams do not exceed a fixed threshold value. Eachof words including coupled words is extracted as a phrase candidate.

The phrase determining section 106 calculates the frequency at whichrespective phrases concurrently occur in the pair of the source languagesentence and the target language sentence. When an i-th source languagephrase is indicated by J[i] and a j-th target language phrase isindicated by E[j], the frequency of concurrence K[i, j] of phrases J[i]and E[j] is calculated by a calculation expression (Ex. 3).$\begin{matrix}{{K\lbrack {I,J} \rbrack} = \frac{\begin{matrix}\{ {{number}\quad{at}\quad{which}\quad{phrase}\quad{J\lbrack i\rbrack}\quad{and}\quad{phrase}\quad{E\lbrack j\rbrack}}  \\ {{concurrently}\quad{occur}\quad{in}\quad{parallel}\text{-}{translation}\quad{sentence}\quad{pair}} \}\end{matrix}}{\begin{matrix}\{ {{{number}\quad{of}\quad{occurences}\quad{of}\quad{phrase}\quad{J\lbrack i\rbrack}} +}  \\{{number}\quad{of}\quad{occurences}\quad{of}\quad{phrase}\quad{E\lbrack j\rbrack}}\end{matrix}}} & \lbrack {{Ex}.\quad 3} \rbrack\end{matrix}$

In an example of FIG. 7, for example, among three parallel-translationsentences 130 which are described as phrase strings, the frequency ofconcurrence of “ONEGAISHIMASU” of the source language phrase and “I'dlike to” of the target language phrase is 2/(2+3), and that of“SHITAINDESUGA” and the target language phrase is 1/(1+3). A phrase pairin which the frequency is not smaller than a fixed value is determinedas parallel-translation phrases, and then registered together with thefrequency and a phrase number in the parallel-translation phrasedictionary 109. Among phrase candidates which have not been determinedas parallel-translation phrases, a word which has been already replacedwith a speech part name is singly registered as a parallel-translationphrase in the parallel-translation phrase dictionary 109. With respectto the other portion, each corresponding word strings in theparallel-translation pair are registered as a pair in a phrasedictionary.

In the example of FIG. 7, for example, phrases are registered in theparallel-translation phrase dictionary 109 as indicated by 131.

After phrases are registered in this way, phrase numbers which concur inone sentence are recorded, and then registered as a phrase number pairin the parallel-translation interphrase rule table 108, as indicated by132 in the example of FIG. 7.

Moreover, a phrase bi-gram of the phrase number pair is obtained, andalso the phrase bi-gram is recorded in the parallel-translationinterphrase rule table 108. Namely, the source language corpus isexpressed by a string of phrase numbers which are registered in theparallel-translation phrase dictionary, a phrase bi-gram is obtained byusing a corpus expressed by phrase numbers, and also the obtainedbi-gram is recorded in the parallel-translation interphrase rule table8. A phrase bi-gram indicating an occurrence probability of phrase jsuccessive to phrase i is expressed by (Ex. 4). $\begin{matrix}\frac{\quad\begin{matrix}\{ {{number}\quad{of}\quad{cases}\quad{in}\quad{which}\quad{phrase}\quad i\quad{and}}  \\ {{phrase}\quad j\quad{occur}\quad{adjacently}\quad{in}\quad{this}\quad{sequence}} \}\end{matrix}}{\{ {{occurence}\quad{number}\quad{of}\quad{phrase}\quad i} \}} & \lbrack {{Ex}.\quad 4} \rbrack\end{matrix}$

In 132 of FIG. 7, for example, a phrase bi-gram of phrase 3 and phrase 1is obtained. With respect to the interphrase rule of phrase 4, phrase 5,and phrase 2, bi-grams of phrase 4 and phrase 5, and phrase 5 and phrase2 are obtained respectively, and then recorded in theparallel-translation interphrase rule table 108.

In the case of interpretation, uttered speech of the source language isfirst input into a speech recognizing section 110. The speechrecognizing section 113 predicts sequentially candidates for arecognized word in time sequence, from a network of words which arewritten as phrases in the parallel-translation phrase dictionary 109 andthe phrase bi-gram written in the parallel-translation interphrase ruletable 108. A sum of an acoustic score based on the distance valuebetween a previously trained acoustic model 113 and the input speech,and a language score based on the phrase bi-gram is set as a recognitionscore, and a continuous word string serving as a recognition candidateis determined by Nbest-search.

The recognized continuous word string is input into a languagetransferring section 111. In the language transferring section 111, theinput continuous word string is transferred into phrase strings in theparallel-translation phrase dictionary 109, and interphrase rulescorresponding to the respective phrase strings are searched. Therecognition resultant sentence of the input source language istransferred into a target language sentence from the target languagephrases which are parallel-translations of the phrases, and theinterphrase rules of the target language.

As described above, in the embodiment, the parallel-translation phrasedictionary 109 and the parallel-translation interphrase rule table 108are used in both the speech recognizing section 110 and the languagetransferring section 111.

The transferred target language sentence is input into an outputsentence producing section 112, and syntactactical unnaturalness iscorrected. For example, optimizations such as addition of articles andindefinite articles, and transference of a verb into the third personsingular form, the plural form, or the past form in a pronoun and a verbare performed. The corrected translation resultant sentence of thetarget language is output, for example, in the form of a text.

In the embodiment described above, rules are described in the form inwhich a source language phrase corresponds to a target language phrase,and recognition is performed in the unit of the phrase. Therefore, alanguage transferring apparatus is enabled in which, even when a portionof an input sentence is an unknown portion sentence or when speechrecognition is partly erroneously performed, a portion that has beencorrectly recognized and analyzed is appropriately processed and output.Furthermore, parallel-translation phrases and interphrase rules areautomatically determined by using the frequency of adjacency of words orparts of speech in each of the source language sentence and the targetlanguage sentence, and concurrent relationships of word strings orspeech part strings of a high frequency in the parallel translation, andinterpretation is performed by using the parallel-translation phraserules. Therefore, a language rule producing apparatus is enabled whichcan automatically and efficiently produce a parallel-translation phrasedictionary of a high quality without requiring much manual assistance.

In the embodiment, the interpreting apparatus has been described as anexample of the language transferring apparatus. This can be similarlyused in another language transferring apparatus, for example, a languagetransferring apparatus which transfers an unliterary uttered sentenceinto a text sentence in written language.

Embodiment 4

In the embodiment also, as an example of a language transferringapparatus, description will be made by using an interpreting apparatuswhich performs transference between different languages, in the samemanner as the third embodiment. FIG. 8 is a block diagram of theinterpreting apparatus of the embodiment.

In the embodiment, a parallel-translation corpus 101, a content worddefinition table 103, a parallel-translation word dictionary 107, amorphological analyzing section 102, a word clastering section usingpart-of-speech 104, a phrase extracting section 142, a phrasedetermining section 143, a parallel-translation interphrase rule table145, a parallel-translation phrase dictionary 144, and a phrasedefinition table 141 constitute an example of the language transferencerule producing apparatus of the invention. The parallel-translationphrase dictionary 144 of the embodiment is an example of the phrasedictionary set forth in claim 6 of the invention.

In the interpreting apparatus of the embodiment, before interpretationis performed, morphemes are first analyzed, and a parallel-translationcorpus in which a speech part tag is given is then produced in the samemanner as the third embodiment.

Next, in accordance with the phrase definition table 141 in which wordor speech part strings that are wished to be extracted as a phrase arepreviously described with being regularized, the phrase extractingsection 142 couples words or parts of speech corresponding to the rules.In an example of 141 of FIG. 9, for example, “O+(verb)+TAI” are coupledas words in accordance with rules such as “verb+auxiliary verb” and“case particle+verb”. With respect to the corpus in which a part ofcontent words are replaced with speech part names and such word orspeech part strings are coupled to be deemed as one word, a frequency ofdoubly chained occurrence (hereinafter, referred to as bi-gram) of eachword or part of speech is calculated. The source language sentence andthe target language sentence are separately subjected to thiscalculation. The calculation expression is identical with (Ex. 2).

In the same manner as the third embodiment, the process is repeateduntil the values of all bi-grams do not exceed a fixed threshold value.Each of words including coupled words is extracted as a phrasecandidate. The phrase determining section produces theparallel-translation phrase dictionary 144 and the parallel-translationinterphrase rule table 145 in the same manner as the third embodiment.In FIG. 9, 151 is an example of the corpus in which words or parts ofspeech are coupled in accordance with the phrase definition table, and152 is an example of the produced parallel-translation phrase dictionary144.

In interpretation also, the embodiment operates in the same manner asthe third embodiment.

In the embodiment described above, words or parts of speech are coupledin accordance with rules of word or speech part strings which are wishedto be deemed as previously defined phrases, and thereafterparallel-translation phrases and interphrase rules are automaticallydetermined by using the frequency of adjacency of words or parts ofspeech in each of the source language sentence and the target languagesentence, and concurrent relationships of word strings or speech partstrings of a high frequency in the parallel translation, and language orstyle transference is performed by using the parallel-translation phraserules. Therefore, it is possible to provide a language transference ruleproducing apparatus which can produce a parallel-translation phrasedictionary of a high quality at a higher efficiency, in a range in whichmanual assistance is suppressed to a minimum level.

The parallel-translation phrase in the embodiment is an example of thecorresponding phrases in the invention.

In the embodiment, the interpreting apparatus has been described as anexample of the language transferring apparatus. This can be similarlyused in another language transferring apparatus, for example, a languagetransferring apparatus which transfers an unliterary uttered sentenceinto a text sentence in written language.

Embodiment 5

In the third embodiment, construction of language rules which are moregeneral and have a high quality is realized by, when the rules are to beconstructed, replacing a part of words in the corpus with speech partnames. Even when words are replaced with semantic codes in place ofspeech part names, it is expected to attain the same effects.Hereinafter, the embodiment will be described with reference to FIG. 10.In the embodiment also, description will be made by using aninterpreting apparatus which performs transference between differentlanguages.

In the embodiment, a parallel-translation corpus 201, a classifiedvocabulary table 216, a parallel-translation word dictionary 207, amorphological analyzing section 202, a semantic coding section 215, aphrase extracting section 205, a phrase determining section 206, aparallel-translation interphrase rule table 208, and aparallel-translation phrase dictionary 209 constitute an example of thelanguage transference rule producing apparatus of the invention. Theparallel-translation phrase dictionary 209 of the embodiment is anexample of the phrase dictionary set forth in claim 6 of the invention.

In the interpreting apparatus of the embodiment, the morphologicalanalyzing section 202 analyzes morphemes of the source language sentencein the parallel-translation corpus 201, thereby giving speech part tagsto the source language sentence. Next, in the morpheme strings of thesource language sentence, the semantic coding section 215 comparesmorphemes with words written in the classified vocabulary table 216.With respect to a morpheme coinciding with a word to which a semanticcode is given in the classified vocabulary table 216, the morpheme nameis replaced with the semantic code, thereby transferring an inputmorpheme string into a morpheme string in which a part of morphemes arereplaced with semantic codes. In this case, it is assumed that amorpheme to be replaced with a semantic code satisfies the followingconditions.

(Conditions) A word which is registered in the parallel-translation worddictionary, and which corresponds to the target language translation inthe parallel-translation word dictionary exists in a correspondingparallel-translation sentence of the target language in the corpus.

In the example of FIG. 11, only “HEYA” and “YOYAKU” which are registeredin the parallel-translation word dictionary, and to which a code isgiven in the classified vocabulary table are replaced with semanticcodes, so that a morpheme string in which these morphemes are replacedwith semantic codes is produced as shown in 2132. Furthermore, also theword names in the parallel-translation sentence of the target languageare replaced with semantic codes as shown in 2133.

With respect to the corpus in which a part of content words are replacedwith semantic codes, the phrase extracting section 205 calculates afrequency of doubly chained occurrence of each word or semantic code.The source language sentence and the target language sentence areseparately subjected to this calculation. The calculation expression isshown in (Ex. 5). $\begin{matrix}\frac{\begin{matrix}\{ {{number}\quad{of}\quad{cases}\quad{in}\quad{which}\quad{word}\quad( {{or}\quad{semantic}\quad{code}} )}  \\ {i\quad{and}\quad{word}\quad( {{or}\quad{semantic}\quad{code}} )j\quad{occur}\quad{adjacently}} \}\end{matrix}}{\begin{matrix}\{ {{total}\quad{number}\quad{of}\quad{occurrences}\quad{of}\quad{word}\quad( {{or}\quad{semantic}\quad{code}} )}  \\ {i + {{total}\quad{number}\quad{of}\quad{occurrences}\quad{of}\quad{word}\quad( {{or}\quad{semantic}\quad{code}} )j}} \}\end{matrix}} & ( {{Ex}.\quad 5} )\end{matrix}$

After a bi-gram is calculated for all of the source language sentencesand the target language sentences in the corpus, the phrase extractingsection couples two words or a semantic code pair of the highestfrequency of occurrence to each other, while assuming the words or thepair as one word. Then, a bi-gram is again calculated. As a result, forexample, word pairs such as “O” and “NEGAI”, “NEGAI” and “SHI”, and“SHI” and “MASU” in each of which the words are adjacent at a higherfrequency are coupled to one another to form a phrase candidate“ONEGAISHIMASU”. In the target language, the word pairs of “I'd” and“like”, and “like” and “to” are coupled to each other.

For each of all of the source language sentences and the target languagesentences, the above-mentioned coupling and calculation of a bi-gram arerepeated until the values of all bi-grams do not exceed a fixedthreshold value. Each of words including coupled words is extracted as aphrase candidate.

In the same manner as the third embodiment, the phrase determiningsection 206 determines parallel-translation phrases, and registers thephrases in the parallel-translation phrase dictionary 209. Moreover, inthe same manner as the third embodiment, interphrase language rules andphrase bi-grams are produced, and then registered in theparallel-translation interphrase rule table 208.

In interpretation also, the embodiment operates in the same manner asthe third embodiment.

In the embodiment described above, rules are described in the form inwhich a source language phrase corresponds to a target language phrase,and recognition is performed in the unit of the phrase. Therefore, alanguage transferring apparatus is enabled in which, even when a portionof an input sentence is an unknown portion sentence or when speechrecognition is partly erroneously performed, a portion that has beencorrectly recognized and analyzed is appropriately processed and output.Furthermore, parallel-translation phrases and interphrase rules areautomatically determined by using the frequency of adjacency of words orsemantic codes in each of the source language sentence and the targetlanguage sentence, and concurrent relationships of word strings orsemantic code strings of a high frequency in the parallel translation,and interpretation is performed by using the parallel-translation phraserules. Therefore, a language rule producing apparatus is enabled whichcan automatically produce a parallel-translation phrase dictionary of ahigh quality without requiring much manual assistance.

In the embodiment, the interpreting apparatus has been described as anexample of the language transferring apparatus. This can be similarlyused in another language transferring apparatus, for example, a languagetransferring apparatus which transfers an unliterary uttered sentenceinto a text sentence in written language or the like.

Embodiment 6

In the fifth embodiment, when the language rules are to be constructed,a phrase is produced by coupling a word or a part of speech, or asemantic code of a high frequency of adjacency. Alternatively, theperplexity of a sentence may be evaluated after a phrase is produced,whereby a phrase which has a higher quality and can ensure a recognitionrate can be produced.

Hereinafter, an embodiment of the language transference rule producingapparatus will be described with reference to FIG. 12.

A parallel-translation phrase dictionary of the embodiment is an exampleof the phrase dictionary set forth in claim 6 of the invention.

In the same manner as the previous embodiment, after morpheme analysis,a semantic coding section 213 produces a parallel-translation corpus inwhich a part of morphemes are transferred into semantic codes.Furthermore, the phrase extracting section calculates a bi-gram of eachword or semantic code. The source language sentence and the targetlanguage sentence are separately subjected to this calculation. Thecalculation expression is identical with (Ex. 5).

In the same manner as the previous embodiment, the process is repeateduntil the values of all bi-grams do not exceed a fixed threshold value.Each of words including coupled words is extracted as a phrasecandidate.

When, in the above process, a bi-gram of each word or semantic code iscalculated and a coupling process is performed depending on the value ofthe bi-gram, a perplexity calculating section 218 calculatesperplexities of cases where word pairs are coupled, and where word pairsare not coupled, and then compares the perplexities. A perplexity iscalculated by (Ex. 6). $\begin{matrix}{{{{Perplexity}\quad F} = 2^{H{(L)}}}{{H(L)} = {- {\sum\limits^{M}\quad{{P( {{{Wi}\text{/}{Wi}} - 1} )}\log\quad{{P( {{{Wi}\text{/}{Wi}} - 1} )}/M}}}}}} & \lbrack {{Ex}.\quad 6} \rbrack\end{matrix}$where P(Wi|Wi−1): probability that an i-th morpheme is Wi when an(i−1)-th morpheme is Wi−1, and M: number of kinds of two-word chains inall corpuses.

A phrase extracting section 217 removes away phrases which are proved asa result of the comparison that the perplexity is increased by couplingwords or semantic codes, from the phrase candidates.

On phrases which remain as phrase candidates after the above process,determination of phrases is performed under the same conditions as thatof the previous embodiment, and a parallel-translation phrase dictionary209 and an interphrase rule table 208 are determined.

In the embodiment described above, when parallel-translation phrases areto be determined, determination is performed by using a perplexity of aparallel-translation corpus in which words are classified by means ofsemantic codes. Therefore, parallel-translation phrases can beautomatically extracted from a corpus, and a parallel-translation phrasedictionary of a high quality can be efficiently produced withoutrequiring much manual assistance. The criterion of a perplexity isclosely related with that of determination on whether a phrase isappropriate for speech recognition or not. Therefore, phrase extractioncan be automatically performed while ensuring recognition accuracy.

In the embodiment, the example wherein phrase extraction is performed byhandling a corpus in which a part of words are replaced with semanticcodes has been described. Even when phrase extraction is performed byhandling a corpus in which a part of words are replaced with speech partnames, it is expected to attain the same effects.

In the fourth embodiment, the example in which the parallel-translationcorpus to which speech part tags are given is handled and phrases areextracted in accordance with the phrase definition table has beendescribed. Also in the case where, as described in the fifth embodiment,a corpus in which a part of words are replaced with semantic codes isused and phrases are extracted in accordance with the phrase definitiontable, it is expected to attain the same effects.

In the first to fifth embodiments, description has been made assumingthat the language transferring apparatus is configured by the speechrecognizing section, the language transferring section, and the outputsentence producing section. The configuration is not restricted to this.As shown in FIG. 13, a speech synthesizing section which performs speechsynthesis on the translation resultant sentence output from an outputsentence producing section 212 may be disposed. The speech synthesizingsection performs speech synthesis by using the parallel-translationinterphrase rule table 208 and the parallel-translation phrasedictionary 209 which are identical with those used in a speechrecognizing section 210 and a language transferring section 211 inspeech synthesis. According to this configuration, even when an inputspeech sentence contains an untrained portion or speech recognition ispartly erroneously performed, the problem that any portion of a speechsynthesis result of the whole sentence is not output can be solved, andit is expected that an adequate speech can be output with respect to aportion that has been correctly recognized.

The whole or a part of functions of components of the languagetransferring apparatus or the language transference rule producingapparatus of the invention may be realized by using a dedicatedhardware, or alternatively by means of software with using computerprograms.

Also a program recording medium which is characterized in that themedium stores a program for causing a computer to execute the whole or apart of the functions of the components of the language transferringapparatus or the language transference rule producing apparatus of theinvention belongs to the invention.

INDUSTRIAL APPLICABILITY

As apparent from the above description, the invention can provide alanguage transference rule producing apparatus and a languagetransferring apparatus which can output a recognition result that can besurely transferred into a target language sentence, and in which, evenwhen a portion of an input sentence is an unknown portion sentence orwhen speech recognition is partly erroneously performed, a portion thathas been correctly recognized and analyzed is therefore appropriatelyprocessed and output.

Furthermore, the invention can provide a language transference ruleproducing apparatus and a language transferring apparatus in which, evenwhen an input speech sentence contains an untrained portion or speechrecognition is partly erroneously performed, transference of only aportion which has been correctly recognized and to which an adequateanalysis rule is applied is enabled, and it is possible to surely outputa partial transference result.

Furthermore, the invention can provide a language transference ruleproducing apparatus in which language rules is enabled to beautomatically produced without requiring much manual assistance.

Furthermore, the invention can provide a language transference ruleproducing apparatus in which language rules of a high quality is enabledto be automatically produced at a higher efficiency without requiringmuch manual assistance.

Furthermore, the invention can provide a language transference ruleproducing apparatus in which language rules of a high quality is enabledto be automatically produced at a higher efficiency.

1. A language transferring apparatus characterized in that saidapparatus comprises: storing means of storing language rules which areobtained by training grammatical or semantic restriction rules for aword or a word string from a training database including a paralleltranslation corpus in which a source language sentence that is input ina form of speech or text, and that has undergone a corresponding targetlanguage transference, is automatically paired with a target languagesentence comprising the source language sentence that has undergone thecorresponding language transference; a speech recognizing section whichperforms speech recognition on input speech by using the stored languagerules, and which outputs a result of the recognition in a form of asentence that is a target language transference; and a languagetransferring section which transfers a sentence that is a targetlanguage transference, into a sentence that has undergone languagetransference, by using the same language rules as that used in saidspeech recognizing section.
 2. A language transferring apparatusaccording to claim 1, characterized in that the language rules areproduced by dividing the sentence that is the target languagetransference, and the transferred sentence into portions in which bothsentences form semantic consistency having style-independent phrases,and making rules with separating language rules in the style-independentphrases from language rules between the style-independent phrases.
 3. Alanguage transferring apparatus according to claim 2, characterized inthat the language rules are produced by making rules on grammatical orsemantic rules in the style-independent phrases and concurrent orconnection relationships between the style-independent phrases.
 4. Alanguage transferring apparatus according to claim 1, characterized inthat said apparatus comprises a speech synthesizing section whichperforms speech synthesis on the sentence that has undergone languagetransference, by using a same language rules as that used in saidlanguage transferring section.
 5. A language transferring apparatusaccording to claim 1, characterized in that said apparatus comprises: arule distance calculating section which, for a language rule group whichis obtained by, among the language rules, bundling language rules of asame target language sentence as a same category, calculates an acousticrule distance of the sentence that is a target language transference oflanguage rules contained in the language rule group; an optimum ruleproducing section which, in order to enhance a recognition level ofspeech recognition, optimizes the rule group by merging language ruleshaving a similar calculated distance.
 6. A language transferringapparatus according to claim 2, characterized in that said apparatuscomprises: a rule distance calculating section which, for a languagerule group which is obtained by, among the language rules, bundlinglanguage rules of a same target language sentence as a same category,calculates an acoustic rule distance of the sentence that is a targetlanguage transference of language rules contained in the language rulegroup; an optimum rule producing section which, in order to enhance arecognition level of speech recognition, optimizes the rule group bymerging language rules having a similar calculated distance.
 7. Alanguage transferring apparatus according to claim 3, characterized inthat said apparatus comprises: a rule distance calculating sectionwhich, for a language rule group which is obtained by, among thelanguage rules, bundling language rules of a same target languagesentence as a same category, calculates an acoustic rule distance of thesentence that is a target language transference of language rulescontained in the language rule group; an optimum rule producing sectionwhich, in order to enhance a recognition level of speech recognition,optimizes the rule group by merging language rules having a similarcalculated distance.
 8. A language transferring apparatus according toclaim 4, characterized in that said apparatus comprises: a rule distancecalculating section which, for a language rule group which is obtainedby, among the language rules, bundling language rules of a same targetlanguage sentence as a same category, calculates an acoustic ruledistance of the sentence that is a target language transference oflanguage rules contained in the language rule group; an optimum ruleproducing section which, in order to enhance a recognition level ofspeech recognition, optimizes the rule group by merging language ruleshaving a similar calculated distance.
 9. The language transferringapparatus, according to claim 1, wherein said speech recognizing sectionand said language transferring section perform each processing bydealing with one bundle defined by a style-independent phrase as acommon language processing unit.